User Tracking for Access Control with Bluetooth Low Energy

Heyn, Robert; Kuhn, Marc; Schulten, Henry; Dumphart, Gregor; Zwyssig, Janick; Trosch, Florian; Wittneben, A.

doi:10.1109/vtcspring.2019.8746465

Cited by 7 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advantages 2, 3, 4 make the paradigm qualified for dynamic settings with time-variant channels. In such circumstances, any scheme that crucially relies on training or calibration would deteriorate heavily (e.g., fingerprinting [21], [22] or parameter estimation with calibrated channel models [4], [15]).…”

Section: Opportunities and Use Casesmentioning

confidence: 99%

“…Thereby, great accuracy and reliability are desired. Important use cases concern indoor environments (retail stores [3], access gates [4], assisted living [5], public transport, warehouses), crowded urban settings [6] and large events [7], vehicular use cases [8], disaster sites, and forests. The related problem of wireless ranging (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Kuhn is with the Zurich University of Applied Sciences (ZHAW), Winterthur, 8400 Switzerland, e-mail: kumn@zhaw.ch. [4], and social distance monitoring for contact tracing in pandemic disease control [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art localization systems deal with these problems with methods such as soft information processing [23], [24] and temporal filtering [4], [25], [26] with the incorporation of inertial measurements [4]. Various recent work [5], [14], [20], [26]- [29] considers multipath as opportunity rather than interference: multipath-assisted localization allows for accuracy improvements if knowledge about the propagation environment is either available a-priori [27] or obtained with mapping [26], [29].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Pairwise Node Localization From Differences in Their UWB Channels to Observer Nodes

Dumphart,

Kramer,

Heyn

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We consider the problem of localization and distance estimation between a pair of wireless nodes in a multipath propagation environment, but not the usual way of processing a channel measurement between them. We propose a novel paradigm which compares the two nodes' ultra-wideband (UWB) channels to other nodes, called observers. The key principle is that the channel impulse responses (CIRs) are similar at small inter-node distance d and differ increasingly with increasing d. We present distance estimators which utilize the rich location information contained in the delay differences of extracted multipath components (MPCs). Likewise, we present estimators for the relative position vector which process both MPC delays and MPC directions. We do so for various important cases: with and without clock synchronization, delay measurement errors, and knowledge of the MPC association between the two CIRs. The estimators exhibit great technological advantages: they do not require precise time-synchronization, line-of-sight conditions, or knowledge about the observer locations or the environment. We study the estimation accuracy with a numerical evaluation based on random sampling and, additionally, with an experimental evaluation based on measurements in an indoor environment. The proposal shows the potential for great accuracy in theory and practice. Integrating the paradigm into existing localization algorithms and systems could enable low-cost localization of wireless users or networks in dynamic multipath settings.

show abstract

Section: Opportunities and Use Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pairwise Node Localization From Differences in Their UWB Channels to Observer Nodes

Dumphart,

Kramer,

Heyn

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The RSS fingerprint-based indoor positioning has been received a lot of interest [ 9 , 10 , 11 , 12 , 13 , 17 , 18 , 19 ]. Recent studies have revealed that this positioning technology has good performance in complex environments (absence of Line of Sight (LoS) signal path), but overall computational cost is not negligible [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Polák

Rozum

Slanina

et al. 2021

Sensors

View full text Add to dashboard Cite

The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

show abstract